In his article titled “Seven Deadly Sins of Sales Forecasting” in the March 28 edition of APICS extra, Fred Tolbert compiled a useful list of bad practices than can worsen our forecasting, inventory management, and customer service results. I particularly liked Deadly Sin #5: Senior Management Meddling, and wrote about it on The Business Forecasting Deal blog. However, I did have some issue with Deadly Sin #1, Using Shipment History, which we will discuss here.
The historical “demand” we feed into our statistical forecasting models play a role in the appropriateness of the forecasts we generate. This history should represent what our customers wanted, and when they wanted it, so any patterns of demand behavior can be projected into the future.
We often misrepresent demand history by attributing demand to the wrong time bucket, or in the wrong quantity. Tolbert shows how easy this can be if we use shipment history to represent demand.
Suppose you receive an order for 1000 units for delivery in July, but are unable to ship until September. If we say that Demand=0 in July (because nothing was shipped) and Demand=1000 in September (when the shipment was made), this doesn’t seem right. The shipments don’t seem to represent the “true demand” of the customer.
Tolbert states, “The appropriate response is to post the 1,000 units as July history for sales forecasting purposes.” But this assumes that Order = Demand, and I’m not convinced this is correct. There are many situations where an order does not represent what the customer truly demands, for example:
- An unfillable order may be rejected by the company or cancelled by the customer (so no “demand” appears in the history).
- An unfilled order may be rolled ahead into future time buckets so “demand” is overstated, re-appearing in each time bucket until the order is filled or cancelled.
- If customers anticipate a shortage, they may inflate their orders in hopes of capturing a larger share of what’s available so “demand” appears higher than it really is.
- If customers anticipate a shortage they may withhold orders, change orders to different (substitute) products, or redirect their orders to alternative suppliers so “demand” appears less than it really is.
“True demand” is a nebulous concept that can be very difficult to capture with the data readily available to us. Unless we service our customers perfectly, in which case Orders = Shipments = Demand, then neither orders nor shipments are a perfect indicator.
Perhaps this Deadly Sin should be restated to read “Assuming you can know true demand” – because you probably can’t. However, as a practical matter for forecasting purposes, it should be good enough to feed our systems with “demand history” that is reasonably close to what true demand really is. When you consider that the typical SKU forecasting error is 30%, 40%, 50% or even more, does it really matter that your history is off by a few percentage points? Probably not.